diff --git a/.github/workflows/lint-and-test.yml b/.github/workflows/lint-and-test.yml index 21ac0c8..edca777 100644 --- a/.github/workflows/lint-and-test.yml +++ b/.github/workflows/lint-and-test.yml @@ -37,4 +37,4 @@ jobs: - name: Run linter run: tox -e lint - name: Run tests - run: tox -p -e py39,py310,py311,py312 + run: tox -p -e py310,py311,py312 diff --git a/.gitignore b/.gitignore index a138e20..21d0061 100644 --- a/.gitignore +++ b/.gitignore @@ -20,4 +20,6 @@ dataset/ weights/ results/ -.coverage* \ No newline at end of file +.coverage* + +*.onnx \ No newline at end of file diff --git a/.python-version b/.python-version index bd28b9c..c8cfe39 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.9 +3.10 diff --git a/README.md b/README.md index 0e43155..aead4d6 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ -![Python](https://img.shields.io/badge/Python-3.9|3.10|3.11|3.12-F9DC3E.svg?logo=python&logoColor=&style=flat) +![Python](https://img.shields.io/badge/Python-3.10|3.11|3.12-F9DC3E.svg?logo=python&logoColor=&style=flat) ![Pytorch](https://img.shields.io/badge/Pytorch-2.5-EE4C2C.svg?logo=Pytorch&style=fla) ![CUDA](https://img.shields.io/badge/CUDA->=11.8-76B900.svg?logo=NVIDIA&style=fla) ![OS](https://img.shields.io/badge/OS-Linux|Mac|Win-1793D1.svg?&style=fla) @@ -61,7 +61,7 @@ yomitoku ${path_data} -f md -o results -v --figure　--lite - `-f`, `--format` 出力形式のファイルフォーマットを指定します。(json, csv, html, md をサポート) - `-o`, `--outdir` 出力先のディレクトリ名を指定します。存在しない場合は新規で作成されます。 - `-v`, `--vis` を指定すると解析結果を可視化した画像を出力します。 -- `-l`, `--lite` を指定すると軽量モデルで推論を実行します。CPUでも高速に推論可能です。 +- `-l`, `--lite` を指定すると軽量モデルで推論を実行します。通常より高速に推論できますが、若干、精度が低下する可能性があります。 - `-d`, `--device` モデルを実行するためのデバイスを指定します。gpu が利用できない場合は cpu で推論が実行されます。(デフォルト: cuda) - `--ignore_line_break` 画像の改行位置を無視して、段落内の文章を連結して返します。（デフォルト：画像通りの改行位置位置で改行します。） - `--figure_letter` 検出した図表に含まれる文字も出力ファイルにエクスポートします。 diff --git a/README_EN.md b/README_EN.md index 0eddbe8..acfe16b 100644 --- a/README_EN.md +++ b/README_EN.md @@ -2,7 +2,7 @@ -![Python](https://img.shields.io/badge/Python-3.9|3.10|3.11|3.12-F9DC3E.svg?logo=python&logoColor=&style=flat) +![Python](https://img.shields.io/badge/Python-3.10|3.11|3.12-F9DC3E.svg?logo=python&logoColor=&style=flat) ![Pytorch](https://img.shields.io/badge/Pytorch-2.5-EE4C2C.svg?logo=Pytorch&style=fla) ![CUDA](https://img.shields.io/badge/CUDA->=11.8-76B900.svg?logo=NVIDIA&style=fla) ![OS](https://img.shields.io/badge/OS-Linux|Mac|Win-1793D1.svg?&style=fla) diff --git a/docs/installation.en.md b/docs/installation.en.md index cd07e9b..50ab095 100644 --- a/docs/installation.en.md +++ b/docs/installation.en.md @@ -1,7 +1,7 @@ # Installation -This package requires Python 3.9 or later and PyTorch 2.5 or later for execution. PyTorch must be installed according to your CUDA version. A GPU with more than 8GB of VRAM is recommended. While it can run on a CPU, please note that the processing is not currently optimized for CPUs, which may result in longer execution times. +This package requires Python 3.10 or later and PyTorch 2.5 or later for execution. PyTorch must be installed according to your CUDA version. A GPU with more than 8GB of VRAM is recommended. While it can run on a CPU, please note that the processing is not currently optimized for CPUs, which may result in longer execution times. ## from PYPI diff --git a/docs/installation.ja.md b/docs/installation.ja.md index 5315e58..4315d7a 100644 --- a/docs/installation.ja.md +++ b/docs/installation.ja.md @@ -1,6 +1,6 @@ # Installation -本パッケージは Python3.9+, Pytorch が実行に必要です。Pytorch はご自身の環境に合わせて、インストールが必要です。計算機は GPU(> VRAM 8G)を推奨しています。CPU でも動作しますが、現在、CPU 向けに処理が最適化されておらず、実行に時間がかかりますのでご注意ください。 +本パッケージは Python3.10+, Pytorch が実行に必要です。Pytorch はご自身の環境に合わせて、インストールが必要です。計算機は GPU(> VRAM 8G)を推奨しています。CPU でも動作しますが、現在、CPU 向けに処理が最適化されておらず、実行に時間がかかりますのでご注意ください。 ## PYPI からインストール diff --git a/pyproject.toml b/pyproject.toml index 0723dc9..0f52047 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ authors = [{name = "Kotaro Kinoshita", email = "kotaro.kinoshita@mlism.com"}] description = "Yomitoku is an AI-powered document image analysis package designed specifically for the Japanese language." readme = "README.md" license = {text = "CC BY-NC-SA 4.0"} -requires-python = ">=3.9,<3.13" +requires-python = ">=3.10,<3.13" keywords = ["Japanese", "OCR", "Deep Learning"] dependencies = [ "huggingface-hub>=0.26.1", @@ -26,6 +26,9 @@ dependencies = [ "torchvision>=0.20.0", "torch>=2.5.0", "pypdfium2>=4.30.0", + "onnx>=1.17.0", + "onnxruntime>=1.20.1", + "onnxruntime-gpu>=1.20.1", ] [tool.uv-dynamic-versioning] @@ -71,7 +74,7 @@ yomitoku = "yomitoku.cli.main:main" [tool.tox] legacy_tox_ini = """ [tox] -envlist = lint, py39, py310, py311, py312, docs +envlist = lint, py310, py311, py312, docs [testenv] deps = pytest diff --git a/src/yomitoku/cli/main.py b/src/yomitoku/cli/main.py index 3f9363e..5a2e226 100644 --- a/src/yomitoku/cli/main.py +++ b/src/yomitoku/cli/main.py @@ -205,6 +205,12 @@ def main(): if args.lite: configs["ocr"]["text_recognizer"]["model_name"] = "parseq-small" + configs["ocr"]["text_detector"]["infer_onnx"] = True + + # Note: Text Detector以外はONNX推論よりもPyTorch推論の方が速いため、ONNX推論は行わない + # configs["ocr"]["text_recognizer"]["infer_onnx"] = True + # configs["layout_analyzer"]["table_structure_recognizer"]["infer_onnx"] = True + # configs["layout_analyzer"]["layout_parser"]["infer_onnx"] = True analyzer = DocumentAnalyzer( configs=configs, diff --git a/src/yomitoku/layout_parser.py b/src/yomitoku/layout_parser.py index 055967e..385db50 100644 --- a/src/yomitoku/layout_parser.py +++ b/src/yomitoku/layout_parser.py @@ -1,11 +1,16 @@ from typing import List, Union import cv2 +import os +import onnx +import onnxruntime import torch import torchvision.transforms as T from PIL import Image from pydantic import conlist +from .constants import ROOT_DIR + from .base import BaseModelCatalog, BaseModule, BaseSchema from .configs import LayoutParserRTDETRv2Config from .models import RTDETRv2 @@ -91,6 +96,7 @@ def __init__( device="cuda", visualize=False, from_pretrained=True, + infer_onnx=False, ): super().__init__() self.load_model(model_name, path_cfg, from_pretrained) @@ -119,11 +125,44 @@ def __init__( } self.role = self._cfg.role + self.infer_onnx = infer_onnx + if infer_onnx: + name = self._cfg.hf_hub_repo.split("/")[-1] + path_onnx = f"{ROOT_DIR}/onnx/{name}.onnx" + if not os.path.exists(path_onnx): + self.convert_onnx(path_onnx) + + model = onnx.load(path_onnx) + if torch.cuda.is_available() and device == "cuda": + self.sess = onnxruntime.InferenceSession( + model.SerializeToString(), providers=["CUDAExecutionProvider"] + ) + else: + self.sess = onnxruntime.InferenceSession(model.SerializeToString()) + + def convert_onnx(self, path_onnx): + dynamic_axes = { + "input": {0: "batch_size"}, + "output": {0: "batch_size"}, + } + + img_size = self._cfg.data.img_size + dummy_input = torch.randn(1, 3, *img_size, requires_grad=True) + + torch.onnx.export( + self.model, + dummy_input, + path_onnx, + opset_version=16, + input_names=["input"], + output_names=["pred_logits", "pred_boxes"], + dynamic_axes=dynamic_axes, + ) def preprocess(self, img): cv_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = Image.fromarray(cv_img) - img_tensor = self.transforms(img)[None].to(self.device) + img_tensor = self.transforms(img)[None] return img_tensor def postprocess(self, preds, image_size): @@ -175,8 +214,19 @@ def __call__(self, img): ori_h, ori_w = img.shape[:2] img_tensor = self.preprocess(img) - with torch.inference_mode(): - preds = self.model(img_tensor) + if self.infer_onnx: + input = img_tensor.numpy() + results = self.sess.run(None, {"input": input}) + preds = { + "pred_logits": torch.tensor(results[0]).to(self.device), + "pred_boxes": torch.tensor(results[1]).to(self.device), + } + + else: + with torch.inference_mode(): + img_tensor = img_tensor.to(self.device) + preds = self.model(img_tensor) + results = self.postprocess(preds, (ori_h, ori_w)) vis = None diff --git a/src/yomitoku/models/parseq.py b/src/yomitoku/models/parseq.py index 4090f2b..684e42d 100644 --- a/src/yomitoku/models/parseq.py +++ b/src/yomitoku/models/parseq.py @@ -22,7 +22,6 @@ from timm.models.helpers import named_apply from torch import Tensor -from ..postprocessor import ParseqTokenizer as Tokenizer from .layers.parseq_transformer import Decoder, Encoder, TokenEmbedding @@ -123,7 +122,6 @@ def decode( def forward( self, - tokenizer: Tokenizer, images: Tensor, max_length: Optional[int] = None, ) -> Tensor: @@ -150,11 +148,11 @@ def forward( if self.decode_ar: tgt_in = torch.full( (bs, num_steps), - tokenizer.pad_id, + self.tokenizer.pad_id, dtype=torch.long, device=self._device, ) - tgt_in[:, 0] = tokenizer.bos_id + tgt_in[:, 0] = self.tokenizer.bos_id logits = [] for i in range(num_steps): @@ -177,7 +175,7 @@ def forward( # greedy decode. add the next token index to the target input tgt_in[:, j] = p_i.squeeze().argmax(-1) # Efficient batch decoding: If all output words have at least one EOS token, end decoding. - if testing and (tgt_in == tokenizer.eos_id).any(dim=-1).all(): + if testing and (tgt_in == self.tokenizer.eos_id).any(dim=-1).all(): break logits = torch.cat(logits, dim=1) @@ -185,7 +183,7 @@ def forward( # No prior context, so input is just . We query all positions. tgt_in = torch.full( (bs, 1), - tokenizer.bos_id, + self.tokenizer.bos_id, dtype=torch.long, device=self._device, ) @@ -200,7 +198,7 @@ def forward( torch.ones( num_steps, num_steps, - dtype=torch.bool, + dtype=torch.int64, device=self._device, ), 2, @@ -208,7 +206,7 @@ def forward( ] = 0 bos = torch.full( (bs, 1), - tokenizer.bos_id, + self.tokenizer.bos_id, dtype=torch.long, device=self._device, ) @@ -216,7 +214,9 @@ def forward( # Prior context is the previous output. tgt_in = torch.cat([bos, logits[:, :-1].argmax(-1)], dim=1) # Mask tokens beyond the first EOS token. - tgt_padding_mask = (tgt_in == tokenizer.eos_id).int().cumsum(-1) > 0 + tgt_padding_mask = (tgt_in == self.tokenizer.eos_id).int().cumsum( + -1 + ) > 0 tgt_out = self.decode( tgt_in, memory, diff --git a/src/yomitoku/onnx/.gitkeep b/src/yomitoku/onnx/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/src/yomitoku/table_structure_recognizer.py b/src/yomitoku/table_structure_recognizer.py index 934b6ba..1f2554b 100644 --- a/src/yomitoku/table_structure_recognizer.py +++ b/src/yomitoku/table_structure_recognizer.py @@ -1,11 +1,16 @@ from typing import List, Union import cv2 +import os +import onnx +import onnxruntime import torch import torchvision.transforms as T from PIL import Image from pydantic import conlist +from .constants import ROOT_DIR + from .base import BaseModelCatalog, BaseModule, BaseSchema from .configs import TableStructureRecognizerRTDETRv2Config from .layout_parser import filter_contained_rectangles_within_category @@ -109,12 +114,13 @@ def __init__( device="cuda", visualize=False, from_pretrained=True, + infer_onnx=False, ): super().__init__() self.load_model( model_name, path_cfg, - from_pretrained=True, + from_pretrained=from_pretrained, ) self.device = device self.visualize = visualize @@ -142,6 +148,40 @@ def __init__( id: category for id, category in enumerate(self._cfg.category) } + self.infer_onnx = infer_onnx + if infer_onnx: + name = self._cfg.hf_hub_repo.split("/")[-1] + path_onnx = f"{ROOT_DIR}/onnx/{name}.onnx" + if not os.path.exists(path_onnx): + self.convert_onnx(path_onnx) + + model = onnx.load(path_onnx) + if torch.cuda.is_available() and device == "cuda": + self.sess = onnxruntime.InferenceSession( + model.SerializeToString(), providers=["CUDAExecutionProvider"] + ) + else: + self.sess = onnxruntime.InferenceSession(model.SerializeToString()) + + def convert_onnx(self, path_onnx): + dynamic_axes = { + "input": {0: "batch_size"}, + "output": {0: "batch_size"}, + } + + img_size = self._cfg.data.img_size + dummy_input = torch.randn(1, 3, *img_size, requires_grad=True) + + torch.onnx.export( + self.model, + dummy_input, + path_onnx, + opset_version=16, + input_names=["input"], + output_names=["pred_logits", "pred_boxes"], + dynamic_axes=dynamic_axes, + ) + def preprocess(self, img, boxes): cv_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) @@ -151,7 +191,7 @@ def preprocess(self, img, boxes): table_img = cv_img[y1:y2, x1:x2, :] th, hw = table_img.shape[:2] table_img = Image.fromarray(table_img) - img_tensor = self.transforms(table_img)[None].to(self.device) + img_tensor = self.transforms(table_img)[None] table_imgs.append( { "tensor": img_tensor, @@ -228,8 +268,19 @@ def __call__(self, img, table_boxes, vis=None): img_tensors = self.preprocess(img, table_boxes) outputs = [] for data in img_tensors: - with torch.inference_mode(): - pred = self.model(data["tensor"]) + if self.infer_onnx: + input = data["tensor"].numpy() + results = self.sess.run(None, {"input": input}) + pred = { + "pred_logits": torch.tensor(results[0]).to(self.device), + "pred_boxes": torch.tensor(results[1]).to(self.device), + } + + else: + with torch.inference_mode(): + data["tensor"] = data["tensor"].to(self.device) + pred = self.model(data["tensor"]) + table = self.postprocess(pred, data) outputs.append(table) diff --git a/src/yomitoku/text_detector.py b/src/yomitoku/text_detector.py index 2931475..cbff462 100644 --- a/src/yomitoku/text_detector.py +++ b/src/yomitoku/text_detector.py @@ -2,6 +2,7 @@ import numpy as np import torch +import os from pydantic import conlist from .base import BaseModelCatalog, BaseModule, BaseSchema @@ -14,6 +15,10 @@ from .models import DBNet from .postprocessor import DBnetPostProcessor from .utils.visualizer import det_visualizer +from .constants import ROOT_DIR + +import onnx +import onnxruntime class TextDetectorModelCatalog(BaseModelCatalog): @@ -43,12 +48,13 @@ def __init__( device="cuda", visualize=False, from_pretrained=True, + infer_onnx=False, ): super().__init__() self.load_model( model_name, path_cfg, - from_pretrained=True, + from_pretrained=from_pretrained, ) self.device = device @@ -58,6 +64,39 @@ def __init__( self.model.to(self.device) self.post_processor = DBnetPostProcessor(**self._cfg.post_process) + self.infer_onnx = infer_onnx + + if infer_onnx: + name = self._cfg.hf_hub_repo.split("/")[-1] + path_onnx = f"{ROOT_DIR}/onnx/{name}.onnx" + if not os.path.exists(path_onnx): + self.convert_onnx(path_onnx) + + model = onnx.load(path_onnx) + if torch.cuda.is_available() and device == "cuda": + self.sess = onnxruntime.InferenceSession( + model.SerializeToString(), providers=["CUDAExecutionProvider"] + ) + else: + self.sess = onnxruntime.InferenceSession(model.SerializeToString()) + + def convert_onnx(self, path_onnx): + dynamic_axes = { + "input": {0: "batch_size", 2: "height", 3: "width"}, + "output": {0: "batch_size", 2: "height", 3: "width"}, + } + + dummy_input = torch.randn(1, 3, 256, 256, requires_grad=True) + + torch.onnx.export( + self.model, + dummy_input, + path_onnx, + opset_version=14, + input_names=["input"], + output_names=["output"], + dynamic_axes=dynamic_axes, + ) def preprocess(self, img): img = img.copy() @@ -81,9 +120,15 @@ def __call__(self, img): ori_h, ori_w = img.shape[:2] tensor = self.preprocess(img) - tensor = tensor.to(self.device) - with torch.inference_mode(): - preds = self.model(tensor) + + if self.infer_onnx: + input = tensor.numpy() + results = self.sess.run(["output"], {"input": input}) + preds = {"binary": torch.tensor(results[0])} + else: + with torch.inference_mode(): + tensor = tensor.to(self.device) + preds = self.model(tensor) quads, scores = self.postprocess(preds, (ori_h, ori_w)) outputs = {"points": quads, "scores": scores} diff --git a/src/yomitoku/text_recognizer.py b/src/yomitoku/text_recognizer.py index 9e52059..5ae0c54 100644 --- a/src/yomitoku/text_recognizer.py +++ b/src/yomitoku/text_recognizer.py @@ -2,6 +2,7 @@ import numpy as np import torch +import os import unicodedata from pydantic import conlist @@ -13,6 +14,10 @@ from .utils.misc import load_charset from .utils.visualizer import rec_visualizer +from .constants import ROOT_DIR +import onnx +import onnxruntime + class TextRecognizerModelCatalog(BaseModelCatalog): def __init__(self): @@ -44,6 +49,7 @@ def __init__( device="cuda", visualize=False, from_pretrained=True, + infer_onnx=False, ): super().__init__() self.load_model( @@ -56,11 +62,28 @@ def __init__( self.device = device + self.model.tokenizer = self.tokenizer self.model.eval() self.model.to(self.device) self.visualize = visualize + self.infer_onnx = infer_onnx + + if infer_onnx: + name = self._cfg.hf_hub_repo.split("/")[-1] + path_onnx = f"{ROOT_DIR}/onnx/{name}.onnx" + if not os.path.exists(path_onnx): + self.convert_onnx(path_onnx) + + model = onnx.load(path_onnx) + if torch.cuda.is_available() and device == "cuda": + self.sess = onnxruntime.InferenceSession( + model.SerializeToString(), providers=["CUDAExecutionProvider"] + ) + else: + self.sess = onnxruntime.InferenceSession(model.SerializeToString()) + def preprocess(self, img, polygons): dataset = ParseqDataset(self._cfg, img, polygons) dataloader = torch.utils.data.DataLoader( @@ -72,6 +95,25 @@ def preprocess(self, img, polygons): return dataloader + def convert_onnx(self, path_onnx): + img_size = self._cfg.data.img_size + input = torch.randn(1, 3, *img_size, requires_grad=True) + dynamic_axes = { + "input": {0: "batch_size"}, + "output": {0: "batch_size"}, + } + + torch.onnx.export( + self.model, + input, + path_onnx, + opset_version=14, + input_names=["input"], + output_names=["output"], + do_constant_folding=True, + dynamic_axes=dynamic_axes, + ) + def postprocess(self, p, points): pred, score = self.tokenizer.decode(p) pred = [unicodedata.normalize("NFKC", x) for x in pred] @@ -102,13 +144,19 @@ def __call__(self, img, points, vis=None): scores = [] directions = [] for data in dataloader: - data = data.to(self.device) - with torch.inference_mode(): - p = self.model(self.tokenizer, data).softmax(-1) - pred, score, direction = self.postprocess(p, points) - preds.extend(pred) - scores.extend(score) - directions.extend(direction) + if self.infer_onnx: + input = data.numpy() + results = self.sess.run(["output"], {"input": input}) + p = torch.tensor(results[0]) + else: + with torch.inference_mode(): + data = data.to(self.device) + p = self.model(data).softmax(-1) + + pred, score, direction = self.postprocess(p, points) + preds.extend(pred) + scores.extend(score) + directions.extend(direction) outputs = { "contents": preds, diff --git a/uv.lock b/uv.lock index 77e07c7..752a52b 100644 --- a/uv.lock +++ b/uv.lock @@ -1,19 +1,14 @@ version = 1 -requires-python = ">=3.9, <3.13" +requires-python = ">=3.10, <3.13" resolution-markers = [ - "python_full_version < '3.10' and platform_system == 'Windows'", - "python_full_version == '3.10.*' and platform_system == 'Windows'", + "python_full_version < '3.11' and platform_system == 'Windows'", "python_full_version == '3.11.*' and platform_system == 'Windows'", "python_full_version >= '3.12' and platform_system == 'Windows'", - "python_full_version < '3.10' and platform_machine == 'arm64' and platform_system == 'Darwin'", - "python_full_version < '3.10' and platform_machine != 'arm64' and platform_system == 'Darwin'", - "python_full_version == '3.10.*' and platform_system == 'Darwin'", + "python_full_version < '3.11' and platform_system == 'Darwin'", "python_full_version == '3.11.*' and platform_system == 'Darwin'", "python_full_version >= '3.12' and platform_system == 'Darwin'", - "python_full_version < '3.10' and platform_machine == 'aarch64' and platform_system == 'Linux'", - "(python_full_version < '3.10' and platform_machine != 'aarch64' and platform_system != 'Darwin' and platform_system != 'Windows') or (python_full_version < '3.10' and platform_system != 'Darwin' and platform_system != 'Linux' and platform_system != 'Windows')", - "python_full_version == '3.10.*' and platform_machine == 'aarch64' and platform_system == 'Linux'", - "(python_full_version == '3.10.*' and platform_machine != 'aarch64' and platform_system != 'Darwin' and platform_system != 'Windows') or (python_full_version == '3.10.*' and platform_system != 'Darwin' and platform_system != 'Linux' and platform_system != 'Windows')", + "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin' and platform_system != 'Windows') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux' and platform_system != 'Windows')", "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux'", "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin' and platform_system != 'Windows') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux' and platform_system != 'Windows')", "python_full_version >= '3.12' and platform_machine == 'aarch64' and platform_system == 'Linux'", @@ -104,21 +99,6 @@ wheels = [ { url = 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